# Influence Diagram

## Introduction

Based on , sections 3.

The paper  explains details about influence diagrams.

## Definition We define the influence diagram as a directed, acyclic graph such that part of its nodes have a finite number of states associated with them

$G=(C,D,V,A,S).$

The sets of nodes consists of chance nodes $C,$ decision nodes $D,$ and value nodes $V$. We index the nodes such that $C∪D=\{1,...,n\}$ and $V=\{n+1,...,n+|V|\}$ where $n=|C|+|D|.$ The set of arcs consists of pairs of nodes such that

$A⊆\{(i,j)∣1≤i<j≤|N|,i∉V\},$

where $|N|=|C|+|D|+|V|.$ The condition enforces that the graph is directed and acyclic, and there are no arcs from value nodes to other nodes.

Each chance and decision node $j∈C∪D$ is associates with a finite number of states $S_j.$ We use integers from one to number of states $|S_j|$ to encode individual states

$S_j=\{1,...,|S_j|\}.$

We define the information set of node $j∈N$ to be its predecessor nodes

$I(j)=\{i∣(i,j)∈A\}.$

Practically, the information set is an edge list to reverse direction in the graph.

## Paths

Paths in influence diagrams represent realizations of states for chance and decision nodes. Formally, a path is a sequence of states

$𝐬=(s_1, s_2, ...,s_n),$

where each state $s_i∈S_i$ for all chance and decision nodes $i∈C∪D.$

We define a subpath of $𝐬$ is a subsequence

$(𝐬_{i_1}, 𝐬_{i_2}, ..., 𝐬_{i_{k}}),$

where $1≤i_1<i_2<...<i_k≤n$ and $k≤n.$

The information path of node $j∈N$ on path $𝐬$ is a subpath defined as

$𝐬_{I(j)}=(𝐬_i ∣ i∈I(j)).$

We define the set of all paths as a product set of all states

$𝐒=∏_{j∈C∪D} S_j.$

The set of information paths of node $j∈N$ is the product set of the states in its information set

$𝐒_{I(j)}=∏_{i∈I(j)} S_i.$

We denote elements of the sets using notation $s_j∈S_j$, $𝐬∈𝐒$, and $𝐬_{I(j)}∈𝐒_{I(j)}.$

## Probabilities

For each chance node $j∈C$, we denote the probability of state $s_j$ given information path $𝐬_{I(j)}$ as

$ℙ(X_j=s_j∣X_{I(j)}=𝐬_{I(j)})=ℙ(s_j∣𝐬_{I(j)})∈[0, 1],$

with

$∑_{s_j∈S_j} ℙ(s_j∣𝐬_{I(j)}) = 1.$

Implementation wise, we can think probabilities as functions of information paths concatenated with state $X_j : 𝐒_{I(j)};S_j → [0, 1]$ where $∑_{s_j∈S_j} X_j(𝐬_{I(j)};s_j)=1.$

## Decision Strategy

For each decision node $j∈D,$ a local decision strategy maps an information path $𝐬_{I(j)}$ to a state $s_j$

$Z_j:𝐒_{I(j)}↦S_j.$

Decision strategy $Z$ contains one local decision strategy for each decision node. Set of all decision strategies is denoted $ℤ.$

A decision stategy $Z∈ℤ$ is compatible with the path $𝐬∈𝐒$ if and only if $Z_j(𝐬_{I(j)})=s_j$ forall $Z_j∈Z$ and $j∈D.$

An active path is path $𝐬∈𝐒$ that is compatible with decision strategy $Z.$ We denote the set of all active paths using $𝐒^Z.$ Since each decision strategy $Z_j$ chooses only one state out of all of its states, the number of active paths is

$|𝐒^Z|=|𝐒|/\prod_{j∈D}|S_j|=\prod_{j∈C}|S_j|.$

## Path Probability

We define the path probability (upper bound) as

$p(𝐬) = ∏_{j∈C} ℙ(𝐬_j∣𝐬_{I(j)}).$

The path probability $ℙ(𝐬∣Z)$ equals $p(𝐬)$ if the path $𝐬$ is compatible with the decision strategy $Z$. Otherwise, the path cannot occur and the probability is zero.

## Consequences

For each value node $j∈V$, we define the consequence given information path $𝐬_{I(j)}$ as

$Y_j:𝐒_{I(j)}↦ℂ,$

where $ℂ$ is the set of consequences. In the code, the consequences are implicit, and we map information paths directly to the utility values.

The utility function maps consequences to real-valued utilities

$U:ℂ↦ℝ.$

## Path Utility

The path utility is defined as the sum of utilities for consequences of value nodes $j∈V$ with information paths $I(j)$

$\mathcal{U}(𝐬) = ∑_{j∈V} U(Y_j(𝐬_{I(j)})).$

## Path Distribution

A path distribution is a pair

$(ℙ(𝐬∣Z), \mathcal{U}(𝐬))$

that comprises of path probability function and path utility function over paths $𝐬∈𝐒$ conditional to the decision strategy $Z.$

• 1Salo, A., Andelmin, J., & Oliveira, F. (2019). Decision Programming for Multi-Stage Optimization under Uncertainty, 1–35. Retrieved from http://arxiv.org/abs/1910.09196
• 2Bielza, C., Gómez, M., & Shenoy, P. P. (2011). A review of representation issues and modeling challenges with influence diagrams. Omega, 39(3), 227–241. https://doi.org/10.1016/j.omega.2010.07.003